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Recently, the domestic COVID-19 epidemic situation is serious, but in publicplaces, some people do not wear masks or wear masks incorrectly, which requiresthe relevant staff to instantly remind and supervise them to wear maskscorrectly. However, in the face of such an important and complicated work, itis very necessary to carry out automated mask-wearing detection in publicplaces. This paper proposes a new mask-wearing detection method based onimproved YOLOv4. Specifically, firstly, we add the Coordinate Attention Moduleto the backbone to coordinate feature fusion and representation. Secondly, weconduct a series of network structural improvements to enhance the modelperformance and robustness. Thirdly, we adaptively deploy the K-meansclustering algorithm to make the nine anchor boxes more suitable for our NPMDdataset. The experiments show that the improved YOLOv4 performs better,exceeding the baseline by 4.06\% AP with a comparable speed of 64.37 FPS.

A New Method on Mask-Wearing Detection for Natural Population Based on Improved YOLOv4